Acoustic Leak Localization Method for Pipelines in High-Noise Environment Using Time-Frequency Signal Segmentation

泄漏 管道运输 多向性 管道(软件) 噪音(视频) 声学 计算机科学 信号(编程语言) 检漏 干扰(通信) 职位(财务) 实时计算 工程类 人工智能 电信 环境工程 图像(数学) 物理 频道(广播) 经济 程序设计语言 节点(物理) 财务
作者
Georgios-Panagiotis Kousiopoulos,Dimitrios Kampelopoulos,Nikolaos Karagiorgos,George-Napoleon Papastavrou,Vasileios Konstantakos,S. Nikolaidis
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:71: 1-11 被引量:38
标识
DOI:10.1109/tim.2022.3150864
摘要

Many practical applications that involve the transportation of fluid products require the use of pipeline networks. A serious problem that emerges in this kind of networks and compromises their safety and normal operation is the occurrence of leaks. Despite the fact that extensive research has been conducted over the years, relative to the development of reliable and efficient leak detection and localization systems, not so much has been presented about pipelines in high-noise environment. This article aims to contribute to the filling of this gap. To this end, a leak localization method is proposed, based on the propagation of acoustic signals in a pipeline when a leak is present. This method employs the use of accelerometers mounted on the external surface of the monitored pipeline, in order to pick up the acoustic–vibrational leak signals. These signals are segmented both in the time and frequency domains and a time-difference-of-arrival (TDOA) algorithm along with statistical analysis is used for the identification of the leak position. The objective of this is to deal with the stochastic nature and the dispersion of the leak acoustic signals and to ensure that the proposed method can easily adapt to different pipelines and can provide efficient localization accuracy even under high-noise conditions. The proposed method was tested experimentally, both in a laboratory setup and in a refinery pipeline with high ambient noise, and the results showed that it can localize leaks efficiently with an average localization error of 4.3%.
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